Your Reliability Engineering Professional Development Site
Find all articles across all article series listed in reverse chronological order.
Not long ago, I walked into a spider’s web on my stairwell. By the next morning, the web was rebuilt! This persistence reminded me of something my mentor, John Moubray, taught me…
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In an increasingly competitive market, the need to bring superior products to market swiftly, cost-effectively, and with minimal risk is paramount. This article presents a summary of the top ten best practices for managing risk in hardware development.
Read on to gain insights into navigating the maze of financial, operational, and market challenges that accompany hardware development. Based upon over 100 years of collective experience with hardware development, the Product Realization Group has distilled these insights from real-world experience. Learn how to reduce risk in your New Product Development and Introduction (NPDI) process and bring better products to market faster, at lower cost, and with less risk.
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Is there any useful result from a parts count prediction?
In most cases that I’ve seen parts count predictions used they are absolutely worthless. Worse, is the folks receiving the results believe they are accurate estimates of reliability performance (or at least use the results as such).
In my opinion, the range of parts count prediction methods and databases harm the field of reliability engineering.
We need to call out the poor results, promote better practices, and stop the vapid use of such a poorly understood tool. [Read more…]
Burn-in testing is a critical reliability testing method used extensively in the electronics and electromechanical industries to identify and eliminate early-life failures in components. This process involves subjecting components or systems to normal or elevated stress conditions to accelerate the occurrence of latent defects. Here’s a detailed exploration of how burn-in testing works, its methodologies, and its importance in ensuring product reliability:
The primary goal of burn-in testing is to detect early failures, also known as infant mortality failures, in components before they are integrated into final products or shipped to customers. These failures typically occur due to defects introduced during the manufacturing process or inherent weaknesses in materials that manifest under operational stress. [Read more…]
THE Plant Wellness Way EAM SYSTEM-OF-RELIABILITY METHODOLOGY ENSURES YOU ALWAYS HAVE LOW MAINTENANCE COSTS AS PART OF THE WORLD CLASS MAINTENANCE, RELIABILITY AND LIFE CYCLE ASSET MANAGEMENT SUCCESS YOU GET
BELOW ARE 14 STRATEGIES A PLANT WELLNESS WAY EAM SYSTEM-OF-RELIABILITY BRINGS YOU THAT ENSURE THE LEAST MAINTENANCE COSTS FOREVERMORE.
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Many larger companies have three critically important operations: organizational sustainability, procurement, and supply chain management. IN most companies, these operations are managed separate from each other, and their operations are not directly influenced by international standards.
Organizational sustainability works to improve all the operations of the company. However, these operations are managed internal to the organization. There may be a sustainability plan for the organization, but it is not used in all facilities or in the same manner for many of the facilities. The corporate sustainability program creates its own program based on how the sustainability manager works with operations. This will be coming to an end as the International Sustainability Standards Board (ISSB) is preparing to release mandatory reporting to provide information to the capital markets.
[Read more…]“Risk comes from not knowing what you’re doing” – Warren Buffett
In this article, I will outline how to evaluate an FMEA against the FMEA Quality Objective for identifying high-risk failures.
One definition for “risk” is “the possibility of loss or injury;” and “high” means “of a greater degree, amount or cost than expected.” Putting these words together, “high risk” means the anticipated loss or injury is too great. [Read more…]
Dear friends, I am happy to release this video on Introduction to Robust Design. In this video, I have briefly explained the philosophy of robust design which was originally created by Dr.Genichi Taguchi. It includes Taguchi’s definition of quality, the quality loss function, Signal to Noise Ratios (SN Ratio), Parameter Diagram, Steps in Robust Design, and an illustration of calculation of SN ratio.
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Hi everyone! In this video, I’m sharing some key insights from the CMC Colombia 2024 conference, where I attended presentations by Ramesh Gulati and Doc Palmer. Ramesh spoke about KPIs and the importance of considering people, not just equipment. Doc Palmer highlighted valuable lessons about planning and scheduling, emphasizing that things don’t have to be perfect to get the job done.
Join me and Doc Palmer as we wrap up the conference with some great takeaways!
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In today’s global economy, supply chain disruptions are inevitable. Companies must proactively plan for resilience and adaptability, whether due to geopolitical tensions, tariffs, component shortages, or factory challenges.
Product Realization Group’s veteran hardware and operations experts Michael Keer, Founder and Managing Partner, and Wayne Miller, New Product Development and Introduction, shared their insights on mitigating supply chain risk, drawing from decades of experience in manufacturing, operations, and supplier management. Below, we outline seven key strategies to safeguard your supply chain and keep your production on track.
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I learned actuarial methods for forecasting and spares planning while working for the US Air Force Logistics Command in the 1970s [AFLCM 66-17 and AFM 400-1]. I am grateful for the education, and I am sorry to report that the USAF has reverted to MTBF management.
The US AFLC actuarial methods were developed for engine management in the 1960s by RAND Corp. [Giesler] They estimated age-interval failure rates and made actuarial forecasts of engine demands depending on the flying-hour program plan. An actuarial forecast is ∑a(s)n(t-s), s=1,2,…,t, where a(s) is actuarial failure rate conditional on survival to age s and n(t-s) is the installed base of age t-s. Periodic meetings consolidated engine lifetime and failure data into agreements on actuarial failure rates, for forecasting engine demands and for war readiness spares requirements.
The USAF actuarial methods assume constant failure rates within short age intervals, Poisson demands, and ignore variance induced by variable flying hours per aircraft in the flying hour program. I later figured out how to estimate actuarial failure rates for all engines, major modules, and their service parts, with or without life-limits and without lifetime data; I computed the distribution of demand forecasts, not Poisson. I offered to show AFIT faculty, AFOSR, AFRL, and RAND how to extend actuarial methods to all service parts [George, 1993].
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A conversation the other day involved how or why someone would use the mean of a set of data described by a Weibull distribution.
The Weibull distribution is great at describing a dataset that has a decreasing or increasing hazard rate over time. Using the distribution we also do not need to determine the MTBF (which is not all that useful, of course).
Walking up the stairs today, I wondered if the arithmetic mean of the time to failure data, commonly used to estimate MTBF, is the same as the mean of the Weibull distribution. Doesn’t everyone think about such things?
Doesn’t everyone think about such things? So, I thought, I’d check. Set up some data with an increasing failure rate, and calculate the arithmetic mean and the Weibull distribution mean. [Read more…]
Early in my quality management career, while working at a small extrusion and fabrication company, I learned something important: bosses pay attention to the money. And if I focused on cost savings projects, I could stay on their good side.
Most of my cost savings efforts at that time focused on eliminating specific types of defects. After all, even a low-frequency defect—especially one that reaches a customer—can drive substantial savings once resolved. Other projects looked inward, targeting inefficiencies in our systems and practices. Lab procedures, control plans, and audit schedules tend to drift out of sync with the products and processes they’re supposed to control. So every now and then, a little system hygiene—an organized cleanup—can free up resources and allow you to reallocate attention to where it’s needed most.
It was during one of those hygiene projects that I stumbled into something I’ve since come to call The Paradox of Invisible Discipline.
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The power of historical failure data is a gold mine of information for reliability engineers. It provides a window into the life cycle of products, revealing patterns and trends that can inform future designs and manufacturing processes. By analyzing this data, we can identify common failure modes, detect early life failures indicating quality or production issues, determine the onset of wear-out stages, and predict time to failure for similar products.
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When you do a Root Cause Failure Analysis or a 5- Why there are no promises that you will actually find the true root cause and fix your problem. Investigating the cause of a failure is fraught with traps, such as making wrong assumptions, insufficient evidence, misinterpreting the evidence, misunderstanding, personal bias and second-guessing. There are necessary issues you need to be aware of that affect the RCA and 5-Why methods, and there are some good practices that you can adopt to improve your chance of doing a successful analysis when applied to equipment failures.
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